A Bayesian Kriging Regression Method to Estimate Air Temperature Using Remote Sensing Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 7; p. 767
• Main Authors: Zhang, Zhenwei, Du, Qingyun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2019
• Subjects: Air temperature; Algorithms; Artificial intelligence; Bayesian analysis; Bayesian framework; Climate; conterminous United States; Ground stations; Heat; Kriging interpolation; Kriging regression; Laboratories; Land surface temperature; Machine learning; Mathematical models; Methods; Parameter estimation; Performance prediction; Regression analysis; Remote sensing; Satellite imagery; Satellites; Spacetime; Spatial data; Statistical analysis; Statistical inference; Statistical methods; Statistical models; Studies; Temperature effects; Uncertainty analysis; Unevenness; Urban areas; Weather stations; New York; United States > US; China

Surface air temperature (Ta) is an important physical quantity, usually measured at ground weather station networks. Measured Ta data is inadequate to characterize the complex spatial patterns of Ta field due to low density and unevenness of the networks. Remote sensing can provide satellite imagery...